{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, LSTM, TimeDistributed, RepeatVector\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_in = 168 #历史数量\n",
    "n_out = 24 #预测数量\n",
    "n_features = 1\n",
    "# n_test = 1\n",
    "n_val = 1\n",
    "n_epochs = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#d导入数据\n",
    "def load_stw_data() -> pd.DataFrame:\n",
    "    \n",
    "    df_stw = pd.read_excel('data3.xlsx')\n",
    "    df_stw.columns = ['BillingDate', 'VolumnHL']\n",
    "    \n",
    "    return df_stw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MinMaxScaler数据归一化，可以帮助网络模型更快的拟合，稍微有一些提高准确率的效果\n",
    "def minmaxscaler(data: pd.DataFrame) -> pd.DataFrame:\n",
    "    \n",
    "    volume = data.VolumnHL.values\n",
    "    volume = volume.reshape(len(volume), 1)\n",
    "    volume = scaler.fit_transform(volume)\n",
    "    volume = volume.reshape(len(volume),)    \n",
    "    data['VolumnHL'] = volume\n",
    "        \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#划分训练数据集和验证数据集,这里需要注意的是我么需要预测的数据是不可以出现在训练中的，切记。\n",
    "def split_data(x, y, n_test: int):\n",
    "    \n",
    "    x_train = x[:-n_val-n_out+1]\n",
    "    x_val = x[-n_val:]\n",
    "    y_train = y[:-n_val-n_out+1]\n",
    "    y_val = y[-n_val:]\n",
    "    \n",
    "    return x_train, y_train, x_val, y_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#划分X和Y\n",
    "def build_train(train, n_in, n_out):\n",
    "    \n",
    "    train = train.drop([\"BillingDate\"], axis=1)\n",
    "    X_train, Y_train = [], []\n",
    "    for i in range(train.shape[0]-n_in-n_out+1):\n",
    "        X_train.append(np.array(train.iloc[i:i+n_in]))\n",
    "        Y_train.append(np.array(train.iloc[i+n_in:i+n_in+n_out][\"VolumnHL\"]))\n",
    "        \n",
    "    return np.array(X_train), np.array(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建最简单的LSTM\n",
    "def build_lstm(n_in: int, n_features: int):\n",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(LSTM(12, activation='relu', input_shape=(n_in, n_features)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(n_out))\n",
    "    model.compile(optimizer='adam', loss='mae')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#模型拟合\n",
    "def model_fit(x_train, y_train, x_val, y_val, n_features):\n",
    "    \n",
    "    model = build_lstm(\n",
    "        n_in   = n_in,\n",
    "        n_features= 1\n",
    "    )\n",
    "    model.compile(loss='mae', optimizer='adam')\n",
    "    model.fit(x_train, y_train, epochs=n_epochs, batch_size=128, verbose=1,  validation_data=(x_val, y_val))\n",
    "    m = model.evaluate(x_val, y_val)\n",
    "    print(m)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_stw_data()\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data = minmaxscaler(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 529 samples, validate on 1 samples\n",
      "Epoch 1/250\n",
      "529/529 [==============================] - 1s 2ms/step - loss: 0.5863 - val_loss: 0.5720\n",
      "Epoch 2/250\n",
      "529/529 [==============================] - 0s 553us/step - loss: 0.5757 - val_loss: 0.5597\n",
      "Epoch 3/250\n",
      "529/529 [==============================] - 0s 585us/step - loss: 0.5647 - val_loss: 0.5466\n",
      "Epoch 4/250\n",
      "529/529 [==============================] - 0s 558us/step - loss: 0.5539 - val_loss: 0.5325\n",
      "Epoch 5/250\n",
      "529/529 [==============================] - 0s 531us/step - loss: 0.5419 - val_loss: 0.5196\n",
      "Epoch 6/250\n",
      "529/529 [==============================] - 0s 579us/step - loss: 0.5283 - val_loss: 0.5045\n",
      "Epoch 7/250\n",
      "529/529 [==============================] - 0s 573us/step - loss: 0.5110 - val_loss: 0.4839\n",
      "Epoch 8/250\n",
      "529/529 [==============================] - 0s 524us/step - loss: 0.4892 - val_loss: 0.4570\n",
      "Epoch 9/250\n",
      "529/529 [==============================] - 0s 573us/step - loss: 0.4584 - val_loss: 0.4308\n",
      "Epoch 10/250\n",
      "529/529 [==============================] - 0s 614us/step - loss: 0.4237 - val_loss: 0.4562\n",
      "Epoch 11/250\n",
      "529/529 [==============================] - 0s 613us/step - loss: 0.4135 - val_loss: 0.4009\n",
      "Epoch 12/250\n",
      "529/529 [==============================] - 0s 438us/step - loss: 0.3934 - val_loss: 0.3876\n",
      "Epoch 13/250\n",
      "529/529 [==============================] - 0s 604us/step - loss: 0.3821 - val_loss: 0.3721\n",
      "Epoch 14/250\n",
      "529/529 [==============================] - 0s 574us/step - loss: 0.3696 - val_loss: 0.3557\n",
      "Epoch 15/250\n",
      "529/529 [==============================] - 0s 694us/step - loss: 0.3502 - val_loss: 0.3493\n",
      "Epoch 16/250\n",
      "529/529 [==============================] - 0s 870us/step - loss: 0.3402 - val_loss: 0.3355\n",
      "Epoch 17/250\n",
      "529/529 [==============================] - 0s 717us/step - loss: 0.3264 - val_loss: 0.3140\n",
      "Epoch 18/250\n",
      "529/529 [==============================] - 0s 680us/step - loss: 0.3137 - val_loss: 0.2987\n",
      "Epoch 19/250\n",
      "529/529 [==============================] - 0s 691us/step - loss: 0.3053 - val_loss: 0.2838\n",
      "Epoch 20/250\n",
      "529/529 [==============================] - 0s 622us/step - loss: 0.2955 - val_loss: 0.2697\n",
      "Epoch 21/250\n",
      "529/529 [==============================] - 0s 729us/step - loss: 0.2875 - val_loss: 0.2582\n",
      "Epoch 22/250\n",
      "529/529 [==============================] - 0s 613us/step - loss: 0.2803 - val_loss: 0.2493\n",
      "Epoch 23/250\n",
      "529/529 [==============================] - 0s 626us/step - loss: 0.2717 - val_loss: 0.2421\n",
      "Epoch 24/250\n",
      "529/529 [==============================] - 0s 560us/step - loss: 0.2696 - val_loss: 0.2334\n",
      "Epoch 25/250\n",
      "529/529 [==============================] - 0s 552us/step - loss: 0.2614 - val_loss: 0.2243\n",
      "Epoch 26/250\n",
      "529/529 [==============================] - 0s 626us/step - loss: 0.2544 - val_loss: 0.2160\n",
      "Epoch 27/250\n",
      "529/529 [==============================] - 0s 660us/step - loss: 0.2499 - val_loss: 0.2077\n",
      "Epoch 28/250\n",
      "529/529 [==============================] - 0s 602us/step - loss: 0.2467 - val_loss: 0.2027\n",
      "Epoch 29/250\n",
      "529/529 [==============================] - 0s 587us/step - loss: 0.2471 - val_loss: 0.1984\n",
      "Epoch 30/250\n",
      "529/529 [==============================] - 0s 603us/step - loss: 0.2396 - val_loss: 0.1948\n",
      "Epoch 31/250\n",
      "529/529 [==============================] - 0s 575us/step - loss: 0.2361 - val_loss: 0.1918\n",
      "Epoch 32/250\n",
      "529/529 [==============================] - 0s 581us/step - loss: 0.2306 - val_loss: 0.1897\n",
      "Epoch 33/250\n",
      "529/529 [==============================] - 0s 591us/step - loss: 0.2340 - val_loss: 0.1884\n",
      "Epoch 34/250\n",
      "529/529 [==============================] - 0s 588us/step - loss: 0.2310 - val_loss: 0.1872\n",
      "Epoch 35/250\n",
      "529/529 [==============================] - 0s 580us/step - loss: 0.2281 - val_loss: 0.1864\n",
      "Epoch 36/250\n",
      "529/529 [==============================] - 0s 575us/step - loss: 0.2274 - val_loss: 0.1822\n",
      "Epoch 37/250\n",
      "529/529 [==============================] - 0s 586us/step - loss: 0.2267 - val_loss: 0.1797\n",
      "Epoch 38/250\n",
      "529/529 [==============================] - 0s 635us/step - loss: 0.2230 - val_loss: 0.1796\n",
      "Epoch 39/250\n",
      "529/529 [==============================] - 0s 576us/step - loss: 0.2214 - val_loss: 0.1794\n",
      "Epoch 40/250\n",
      "529/529 [==============================] - 0s 585us/step - loss: 0.2237 - val_loss: 0.1779\n",
      "Epoch 41/250\n",
      "529/529 [==============================] - 0s 590us/step - loss: 0.2183 - val_loss: 0.1778\n",
      "Epoch 42/250\n",
      "529/529 [==============================] - 0s 570us/step - loss: 0.2186 - val_loss: 0.1798\n",
      "Epoch 43/250\n",
      "529/529 [==============================] - 0s 579us/step - loss: 0.2176 - val_loss: 0.1791\n",
      "Epoch 44/250\n",
      "529/529 [==============================] - 0s 582us/step - loss: 0.2173 - val_loss: 0.1775\n",
      "Epoch 45/250\n",
      "529/529 [==============================] - 0s 581us/step - loss: 0.2166 - val_loss: 0.1810\n",
      "Epoch 46/250\n",
      "529/529 [==============================] - 0s 593us/step - loss: 0.2133 - val_loss: 0.1784\n",
      "Epoch 47/250\n",
      "529/529 [==============================] - 0s 567us/step - loss: 0.2143 - val_loss: 0.1751\n",
      "Epoch 48/250\n",
      "529/529 [==============================] - 0s 579us/step - loss: 0.2106 - val_loss: 0.1808\n",
      "Epoch 49/250\n",
      "529/529 [==============================] - 0s 601us/step - loss: 0.2101 - val_loss: 0.1794\n",
      "Epoch 50/250\n",
      "529/529 [==============================] - 0s 581us/step - loss: 0.2093 - val_loss: 0.1741\n",
      "Epoch 51/250\n",
      "529/529 [==============================] - 0s 589us/step - loss: 0.2092 - val_loss: 0.1759\n",
      "Epoch 52/250\n",
      "529/529 [==============================] - 0s 574us/step - loss: 0.2069 - val_loss: 0.1721\n",
      "Epoch 53/250\n",
      "529/529 [==============================] - 0s 594us/step - loss: 0.2093 - val_loss: 0.1702\n",
      "Epoch 54/250\n",
      "529/529 [==============================] - 0s 589us/step - loss: 0.2079 - val_loss: 0.1686\n",
      "Epoch 55/250\n",
      "529/529 [==============================] - 0s 581us/step - loss: 0.2078 - val_loss: 0.1669\n",
      "Epoch 56/250\n",
      "529/529 [==============================] - 0s 589us/step - loss: 0.2069 - val_loss: 0.1704\n",
      "Epoch 57/250\n",
      "529/529 [==============================] - 0s 579us/step - loss: 0.2055 - val_loss: 0.1718\n",
      "Epoch 58/250\n",
      "529/529 [==============================] - 0s 588us/step - loss: 0.2062 - val_loss: 0.1692\n",
      "Epoch 59/250\n",
      "529/529 [==============================] - 0s 592us/step - loss: 0.2028 - val_loss: 0.1686\n",
      "Epoch 60/250\n",
      "529/529 [==============================] - 0s 584us/step - loss: 0.2048 - val_loss: 0.1688\n",
      "Epoch 61/250\n",
      "529/529 [==============================] - 0s 592us/step - loss: 0.2013 - val_loss: 0.1653\n",
      "Epoch 62/250\n",
      "529/529 [==============================] - 0s 587us/step - loss: 0.2005 - val_loss: 0.1676\n",
      "Epoch 63/250\n",
      "529/529 [==============================] - 0s 584us/step - loss: 0.2040 - val_loss: 0.1698\n",
      "Epoch 64/250\n",
      "529/529 [==============================] - 0s 665us/step - loss: 0.2006 - val_loss: 0.1689\n",
      "Epoch 65/250\n",
      "529/529 [==============================] - 0s 638us/step - loss: 0.2001 - val_loss: 0.1675\n",
      "Epoch 66/250\n",
      "529/529 [==============================] - 0s 609us/step - loss: 0.1986 - val_loss: 0.1662\n",
      "Epoch 67/250\n",
      "529/529 [==============================] - 0s 587us/step - loss: 0.1985 - val_loss: 0.1619\n",
      "Epoch 68/250\n",
      "529/529 [==============================] - 0s 641us/step - loss: 0.1970 - val_loss: 0.1591\n",
      "Epoch 69/250\n",
      "529/529 [==============================] - 0s 561us/step - loss: 0.1939 - val_loss: 0.1572\n",
      "Epoch 70/250\n",
      "529/529 [==============================] - 0s 588us/step - loss: 0.1947 - val_loss: 0.1582\n",
      "Epoch 71/250\n",
      "529/529 [==============================] - 0s 577us/step - loss: 0.1926 - val_loss: 0.1567\n",
      "Epoch 72/250\n",
      "529/529 [==============================] - 0s 591us/step - loss: 0.1893 - val_loss: 0.1580\n",
      "Epoch 73/250\n",
      "529/529 [==============================] - 0s 555us/step - loss: 0.1908 - val_loss: 0.1576\n",
      "Epoch 74/250\n",
      "529/529 [==============================] - 0s 581us/step - loss: 0.1942 - val_loss: 0.1544\n",
      "Epoch 75/250\n",
      "529/529 [==============================] - 0s 605us/step - loss: 0.1907 - val_loss: 0.1501\n",
      "Epoch 76/250\n",
      "529/529 [==============================] - 0s 668us/step - loss: 0.1956 - val_loss: 0.1475\n",
      "Epoch 77/250\n",
      "529/529 [==============================] - 0s 578us/step - loss: 0.1905 - val_loss: 0.1475\n",
      "Epoch 78/250\n",
      "529/529 [==============================] - 0s 582us/step - loss: 0.1908 - val_loss: 0.1465\n",
      "Epoch 79/250\n",
      "529/529 [==============================] - 0s 602us/step - loss: 0.1865 - val_loss: 0.1455\n",
      "Epoch 80/250\n",
      "529/529 [==============================] - 0s 570us/step - loss: 0.1931 - val_loss: 0.1473\n",
      "Epoch 81/250\n",
      "529/529 [==============================] - 0s 580us/step - loss: 0.1883 - val_loss: 0.1481\n",
      "Epoch 82/250\n",
      "529/529 [==============================] - 0s 590us/step - loss: 0.1862 - val_loss: 0.1437\n",
      "Epoch 83/250\n",
      "529/529 [==============================] - 0s 582us/step - loss: 0.1825 - val_loss: 0.1395\n",
      "Epoch 84/250\n",
      "529/529 [==============================] - 0s 596us/step - loss: 0.1828 - val_loss: 0.1371\n",
      "Epoch 85/250\n",
      "529/529 [==============================] - 0s 580us/step - loss: 0.1848 - val_loss: 0.1384\n",
      "Epoch 86/250\n",
      "529/529 [==============================] - 0s 602us/step - loss: 0.1859 - val_loss: 0.1379\n",
      "Epoch 87/250\n",
      "529/529 [==============================] - 0s 596us/step - loss: 0.1823 - val_loss: 0.1328\n",
      "Epoch 88/250\n",
      "529/529 [==============================] - 0s 595us/step - loss: 0.1836 - val_loss: 0.1292\n",
      "Epoch 89/250\n",
      "529/529 [==============================] - 0s 564us/step - loss: 0.1810 - val_loss: 0.1292\n",
      "Epoch 90/250\n",
      "529/529 [==============================] - 0s 598us/step - loss: 0.1782 - val_loss: 0.1287\n",
      "Epoch 91/250\n",
      "529/529 [==============================] - 0s 591us/step - loss: 0.1811 - val_loss: 0.1266\n",
      "Epoch 92/250\n",
      "529/529 [==============================] - 0s 764us/step - loss: 0.1809 - val_loss: 0.1247\n",
      "Epoch 93/250\n",
      "529/529 [==============================] - 0s 572us/step - loss: 0.1806 - val_loss: 0.1250\n",
      "Epoch 94/250\n",
      "529/529 [==============================] - 0s 610us/step - loss: 0.1773 - val_loss: 0.1257\n",
      "Epoch 95/250\n",
      "529/529 [==============================] - 0s 607us/step - loss: 0.1754 - val_loss: 0.1235\n",
      "Epoch 96/250\n",
      "529/529 [==============================] - 0s 598us/step - loss: 0.1734 - val_loss: 0.1199\n",
      "Epoch 97/250\n",
      "529/529 [==============================] - 0s 599us/step - loss: 0.1713 - val_loss: 0.1180\n",
      "Epoch 98/250\n",
      "529/529 [==============================] - 0s 632us/step - loss: 0.1738 - val_loss: 0.1179\n",
      "Epoch 99/250\n",
      "529/529 [==============================] - 0s 667us/step - loss: 0.1725 - val_loss: 0.1189\n",
      "Epoch 100/250\n",
      "529/529 [==============================] - 0s 589us/step - loss: 0.1708 - val_loss: 0.1198\n",
      "Epoch 101/250\n",
      "529/529 [==============================] - 0s 634us/step - loss: 0.1751 - val_loss: 0.1180\n",
      "Epoch 102/250\n",
      "529/529 [==============================] - 0s 589us/step - loss: 0.1749 - val_loss: 0.1156\n",
      "Epoch 103/250\n",
      "529/529 [==============================] - 0s 596us/step - loss: 0.1693 - val_loss: 0.1151\n",
      "Epoch 104/250\n",
      "529/529 [==============================] - 0s 593us/step - loss: 0.1677 - val_loss: 0.1167\n",
      "Epoch 105/250\n",
      "529/529 [==============================] - 0s 603us/step - loss: 0.1654 - val_loss: 0.1165\n",
      "Epoch 106/250\n",
      "529/529 [==============================] - 0s 596us/step - loss: 0.1665 - val_loss: 0.1159\n",
      "Epoch 107/250\n",
      "529/529 [==============================] - 0s 593us/step - loss: 0.1645 - val_loss: 0.1146\n",
      "Epoch 108/250\n",
      "529/529 [==============================] - 0s 572us/step - loss: 0.1641 - val_loss: 0.1131\n",
      "Epoch 109/250\n",
      "529/529 [==============================] - 0s 612us/step - loss: 0.1658 - val_loss: 0.1120\n",
      "Epoch 110/250\n",
      "529/529 [==============================] - 0s 597us/step - loss: 0.1640 - val_loss: 0.1103\n",
      "Epoch 111/250\n",
      "529/529 [==============================] - 0s 605us/step - loss: 0.1604 - val_loss: 0.1106\n",
      "Epoch 112/250\n",
      "529/529 [==============================] - 0s 599us/step - loss: 0.1631 - val_loss: 0.1097\n",
      "Epoch 113/250\n",
      "529/529 [==============================] - 0s 608us/step - loss: 0.1641 - val_loss: 0.1094\n",
      "Epoch 114/250\n",
      "529/529 [==============================] - 0s 585us/step - loss: 0.1639 - val_loss: 0.1059\n",
      "Epoch 115/250\n",
      "529/529 [==============================] - 0s 606us/step - loss: 0.1593 - val_loss: 0.1066\n",
      "Epoch 116/250\n",
      "529/529 [==============================] - 0s 605us/step - loss: 0.1598 - val_loss: 0.1065\n",
      "Epoch 117/250\n",
      "529/529 [==============================] - 0s 585us/step - loss: 0.1597 - val_loss: 0.1078\n",
      "Epoch 118/250\n",
      "529/529 [==============================] - 0s 579us/step - loss: 0.1625 - val_loss: 0.1057\n",
      "Epoch 119/250\n",
      "529/529 [==============================] - 0s 592us/step - loss: 0.1619 - val_loss: 0.1041\n",
      "Epoch 120/250\n",
      "529/529 [==============================] - 0s 599us/step - loss: 0.1580 - val_loss: 0.1035\n",
      "Epoch 121/250\n",
      "529/529 [==============================] - 0s 597us/step - loss: 0.1610 - val_loss: 0.1038\n",
      "Epoch 122/250\n",
      "529/529 [==============================] - 0s 645us/step - loss: 0.1571 - val_loss: 0.1050\n",
      "Epoch 123/250\n",
      "529/529 [==============================] - 0s 625us/step - loss: 0.1547 - val_loss: 0.1071\n",
      "Epoch 124/250\n",
      "529/529 [==============================] - 0s 604us/step - loss: 0.1555 - val_loss: 0.1081\n",
      "Epoch 125/250\n",
      "529/529 [==============================] - 0s 616us/step - loss: 0.1559 - val_loss: 0.1074\n",
      "Epoch 126/250\n",
      "529/529 [==============================] - 0s 662us/step - loss: 0.1580 - val_loss: 0.1038\n",
      "Epoch 127/250\n",
      "529/529 [==============================] - 0s 690us/step - loss: 0.1544 - val_loss: 0.1015\n",
      "Epoch 128/250\n",
      "529/529 [==============================] - 0s 675us/step - loss: 0.1559 - val_loss: 0.1023\n",
      "Epoch 129/250\n",
      "529/529 [==============================] - 0s 598us/step - loss: 0.1530 - val_loss: 0.1030\n",
      "Epoch 130/250\n",
      "529/529 [==============================] - 0s 591us/step - loss: 0.1543 - val_loss: 0.1048\n",
      "Epoch 131/250\n",
      "529/529 [==============================] - 0s 594us/step - loss: 0.1535 - val_loss: 0.1041\n",
      "Epoch 132/250\n",
      "529/529 [==============================] - 0s 602us/step - loss: 0.1470 - val_loss: 0.1029\n",
      "Epoch 133/250\n",
      "529/529 [==============================] - 0s 595us/step - loss: 0.1542 - val_loss: 0.1017\n",
      "Epoch 134/250\n",
      "529/529 [==============================] - 0s 606us/step - loss: 0.1481 - val_loss: 0.1004\n",
      "Epoch 135/250\n",
      "529/529 [==============================] - 0s 599us/step - loss: 0.1489 - val_loss: 0.1010\n",
      "Epoch 136/250\n",
      "529/529 [==============================] - 0s 595us/step - loss: 0.1450 - val_loss: 0.1016\n",
      "Epoch 137/250\n",
      "529/529 [==============================] - 0s 582us/step - loss: 0.1456 - val_loss: 0.1021\n",
      "Epoch 138/250\n",
      "529/529 [==============================] - 0s 600us/step - loss: 0.1456 - val_loss: 0.1018\n",
      "Epoch 139/250\n",
      "529/529 [==============================] - 0s 593us/step - loss: 0.1450 - val_loss: 0.1015\n",
      "Epoch 140/250\n",
      "529/529 [==============================] - 0s 590us/step - loss: 0.1477 - val_loss: 0.1022\n",
      "Epoch 141/250\n",
      "529/529 [==============================] - 0s 595us/step - loss: 0.1480 - val_loss: 0.1035\n",
      "Epoch 142/250\n",
      "529/529 [==============================] - 0s 588us/step - loss: 0.1451 - val_loss: 0.1034\n",
      "Epoch 143/250\n",
      "529/529 [==============================] - 0s 584us/step - loss: 0.1452 - val_loss: 0.1024\n",
      "Epoch 144/250\n",
      "529/529 [==============================] - 0s 614us/step - loss: 0.1492 - val_loss: 0.0998\n",
      "Epoch 145/250\n",
      "529/529 [==============================] - 0s 589us/step - loss: 0.1468 - val_loss: 0.0979\n",
      "Epoch 146/250\n",
      "529/529 [==============================] - 0s 589us/step - loss: 0.1469 - val_loss: 0.0973\n",
      "Epoch 147/250\n",
      "529/529 [==============================] - 0s 589us/step - loss: 0.1447 - val_loss: 0.0981\n",
      "Epoch 148/250\n",
      "529/529 [==============================] - 0s 600us/step - loss: 0.1436 - val_loss: 0.0997\n",
      "Epoch 149/250\n",
      "529/529 [==============================] - 0s 575us/step - loss: 0.1448 - val_loss: 0.1017\n",
      "Epoch 150/250\n",
      "529/529 [==============================] - 0s 567us/step - loss: 0.1471 - val_loss: 0.1018\n",
      "Epoch 151/250\n",
      "529/529 [==============================] - 0s 580us/step - loss: 0.1426 - val_loss: 0.1014\n",
      "Epoch 152/250\n",
      "529/529 [==============================] - 0s 579us/step - loss: 0.1432 - val_loss: 0.1002\n",
      "Epoch 153/250\n",
      "529/529 [==============================] - 0s 603us/step - loss: 0.1393 - val_loss: 0.0993\n",
      "Epoch 154/250\n",
      "529/529 [==============================] - 0s 599us/step - loss: 0.1432 - val_loss: 0.0978\n",
      "Epoch 155/250\n",
      "529/529 [==============================] - 0s 633us/step - loss: 0.1453 - val_loss: 0.0960\n",
      "Epoch 156/250\n",
      "529/529 [==============================] - 0s 555us/step - loss: 0.1460 - val_loss: 0.0950\n",
      "Epoch 157/250\n",
      "529/529 [==============================] - 0s 658us/step - loss: 0.1415 - val_loss: 0.0949\n",
      "Epoch 158/250\n",
      "529/529 [==============================] - 0s 670us/step - loss: 0.1445 - val_loss: 0.0959\n",
      "Epoch 159/250\n",
      "529/529 [==============================] - 0s 632us/step - loss: 0.1426 - val_loss: 0.0974\n",
      "Epoch 160/250\n",
      "529/529 [==============================] - 0s 636us/step - loss: 0.1360 - val_loss: 0.0988\n",
      "Epoch 161/250\n",
      "529/529 [==============================] - 0s 610us/step - loss: 0.1410 - val_loss: 0.0994\n",
      "Epoch 162/250\n",
      "529/529 [==============================] - 0s 604us/step - loss: 0.1368 - val_loss: 0.0977\n",
      "Epoch 163/250\n",
      "529/529 [==============================] - 0s 579us/step - loss: 0.1419 - val_loss: 0.0967\n",
      "Epoch 164/250\n",
      "529/529 [==============================] - 0s 591us/step - loss: 0.1363 - val_loss: 0.0962\n",
      "Epoch 165/250\n",
      "529/529 [==============================] - 0s 600us/step - loss: 0.1352 - val_loss: 0.0960\n",
      "Epoch 166/250\n",
      "529/529 [==============================] - 0s 571us/step - loss: 0.1350 - val_loss: 0.0954\n",
      "Epoch 167/250\n",
      "529/529 [==============================] - 0s 571us/step - loss: 0.1381 - val_loss: 0.0961\n",
      "Epoch 168/250\n",
      "529/529 [==============================] - 0s 694us/step - loss: 0.1359 - val_loss: 0.0962\n",
      "Epoch 169/250\n",
      "529/529 [==============================] - 0s 594us/step - loss: 0.1374 - val_loss: 0.0957\n",
      "Epoch 170/250\n",
      "529/529 [==============================] - 0s 617us/step - loss: 0.1341 - val_loss: 0.0961\n",
      "Epoch 171/250\n",
      "529/529 [==============================] - 0s 596us/step - loss: 0.1348 - val_loss: 0.0964\n",
      "Epoch 172/250\n",
      "529/529 [==============================] - 0s 590us/step - loss: 0.1366 - val_loss: 0.0966\n",
      "Epoch 173/250\n",
      "529/529 [==============================] - 0s 568us/step - loss: 0.1375 - val_loss: 0.0961\n",
      "Epoch 174/250\n",
      "529/529 [==============================] - 0s 573us/step - loss: 0.1397 - val_loss: 0.0958\n",
      "Epoch 175/250\n",
      "529/529 [==============================] - 0s 600us/step - loss: 0.1364 - val_loss: 0.0961\n",
      "Epoch 176/250\n",
      "529/529 [==============================] - 0s 611us/step - loss: 0.1357 - val_loss: 0.0961\n",
      "Epoch 177/250\n",
      "529/529 [==============================] - 0s 566us/step - loss: 0.1386 - val_loss: 0.0953\n",
      "Epoch 178/250\n",
      "529/529 [==============================] - 0s 578us/step - loss: 0.1316 - val_loss: 0.0943\n",
      "Epoch 179/250\n",
      "529/529 [==============================] - 0s 596us/step - loss: 0.1306 - val_loss: 0.0921\n",
      "Epoch 180/250\n",
      "529/529 [==============================] - 0s 598us/step - loss: 0.1384 - val_loss: 0.0920\n",
      "Epoch 181/250\n",
      "529/529 [==============================] - 0s 599us/step - loss: 0.1312 - val_loss: 0.0920\n",
      "Epoch 182/250\n",
      "529/529 [==============================] - 0s 593us/step - loss: 0.1332 - val_loss: 0.0922\n",
      "Epoch 183/250\n",
      "529/529 [==============================] - 0s 581us/step - loss: 0.1355 - val_loss: 0.0915\n",
      "Epoch 184/250\n",
      "529/529 [==============================] - 0s 651us/step - loss: 0.1287 - val_loss: 0.0913\n",
      "Epoch 185/250\n",
      "529/529 [==============================] - 0s 712us/step - loss: 0.1311 - val_loss: 0.0920\n",
      "Epoch 186/250\n",
      "529/529 [==============================] - 0s 626us/step - loss: 0.1325 - val_loss: 0.0916\n",
      "Epoch 187/250\n",
      "529/529 [==============================] - 0s 593us/step - loss: 0.1360 - val_loss: 0.0906\n",
      "Epoch 188/250\n",
      "529/529 [==============================] - 0s 626us/step - loss: 0.1311 - val_loss: 0.0905\n",
      "Epoch 189/250\n",
      "529/529 [==============================] - 0s 568us/step - loss: 0.1337 - val_loss: 0.0900\n",
      "Epoch 190/250\n",
      "529/529 [==============================] - 0s 602us/step - loss: 0.1332 - val_loss: 0.0913\n",
      "Epoch 191/250\n",
      "529/529 [==============================] - 0s 618us/step - loss: 0.1289 - val_loss: 0.0920\n",
      "Epoch 192/250\n",
      "529/529 [==============================] - 0s 603us/step - loss: 0.1268 - val_loss: 0.0915\n",
      "Epoch 193/250\n",
      "529/529 [==============================] - 0s 620us/step - loss: 0.1266 - val_loss: 0.0915\n",
      "Epoch 194/250\n",
      "529/529 [==============================] - 0s 595us/step - loss: 0.1281 - val_loss: 0.0914\n",
      "Epoch 195/250\n",
      "529/529 [==============================] - 0s 645us/step - loss: 0.1263 - val_loss: 0.0923\n",
      "Epoch 196/250\n",
      "529/529 [==============================] - 0s 679us/step - loss: 0.1273 - val_loss: 0.0929\n",
      "Epoch 197/250\n",
      "529/529 [==============================] - 0s 608us/step - loss: 0.1330 - val_loss: 0.0923\n",
      "Epoch 198/250\n",
      "529/529 [==============================] - 0s 559us/step - loss: 0.1272 - val_loss: 0.0923\n",
      "Epoch 199/250\n",
      "529/529 [==============================] - 0s 632us/step - loss: 0.1302 - val_loss: 0.0915\n",
      "Epoch 200/250\n",
      "529/529 [==============================] - 0s 588us/step - loss: 0.1302 - val_loss: 0.0901\n",
      "Epoch 201/250\n",
      "529/529 [==============================] - 0s 601us/step - loss: 0.1297 - val_loss: 0.0911\n",
      "Epoch 202/250\n",
      "529/529 [==============================] - 0s 618us/step - loss: 0.1307 - val_loss: 0.0911\n",
      "Epoch 203/250\n",
      "529/529 [==============================] - 0s 610us/step - loss: 0.1305 - val_loss: 0.0908\n",
      "Epoch 204/250\n",
      "529/529 [==============================] - 0s 612us/step - loss: 0.1284 - val_loss: 0.0910\n",
      "Epoch 205/250\n",
      "529/529 [==============================] - 0s 678us/step - loss: 0.1250 - val_loss: 0.0918\n",
      "Epoch 206/250\n",
      "529/529 [==============================] - 0s 714us/step - loss: 0.1257 - val_loss: 0.0912\n",
      "Epoch 207/250\n",
      "529/529 [==============================] - 0s 594us/step - loss: 0.1292 - val_loss: 0.0910\n",
      "Epoch 208/250\n",
      "529/529 [==============================] - 0s 618us/step - loss: 0.1228 - val_loss: 0.0918\n",
      "Epoch 209/250\n",
      "529/529 [==============================] - 0s 591us/step - loss: 0.1249 - val_loss: 0.0904\n",
      "Epoch 210/250\n",
      "529/529 [==============================] - 0s 588us/step - loss: 0.1268 - val_loss: 0.0911\n",
      "Epoch 211/250\n",
      "529/529 [==============================] - 0s 570us/step - loss: 0.1259 - val_loss: 0.0924\n",
      "Epoch 212/250\n",
      "529/529 [==============================] - 0s 597us/step - loss: 0.1272 - val_loss: 0.0923\n",
      "Epoch 213/250\n",
      "529/529 [==============================] - 0s 571us/step - loss: 0.1260 - val_loss: 0.0926\n",
      "Epoch 214/250\n",
      "529/529 [==============================] - 0s 604us/step - loss: 0.1286 - val_loss: 0.0911\n",
      "Epoch 215/250\n",
      "529/529 [==============================] - 0s 658us/step - loss: 0.1241 - val_loss: 0.0893\n",
      "Epoch 216/250\n",
      "529/529 [==============================] - 0s 653us/step - loss: 0.1221 - val_loss: 0.0901\n",
      "Epoch 217/250\n",
      "529/529 [==============================] - 0s 635us/step - loss: 0.1293 - val_loss: 0.0893\n",
      "Epoch 218/250\n",
      "529/529 [==============================] - 0s 514us/step - loss: 0.1243 - val_loss: 0.0896\n",
      "Epoch 219/250\n",
      "529/529 [==============================] - 0s 597us/step - loss: 0.1278 - val_loss: 0.0903\n",
      "Epoch 220/250\n",
      "529/529 [==============================] - 0s 628us/step - loss: 0.1253 - val_loss: 0.0907\n",
      "Epoch 221/250\n",
      "529/529 [==============================] - 0s 599us/step - loss: 0.1251 - val_loss: 0.0908\n",
      "Epoch 222/250\n",
      "529/529 [==============================] - 0s 562us/step - loss: 0.1232 - val_loss: 0.0904\n",
      "Epoch 223/250\n",
      "529/529 [==============================] - 0s 692us/step - loss: 0.1218 - val_loss: 0.0902\n",
      "Epoch 224/250\n",
      "529/529 [==============================] - 0s 660us/step - loss: 0.1232 - val_loss: 0.0901\n",
      "Epoch 225/250\n",
      "529/529 [==============================] - 0s 565us/step - loss: 0.1202 - val_loss: 0.0897\n",
      "Epoch 226/250\n",
      "529/529 [==============================] - 0s 609us/step - loss: 0.1235 - val_loss: 0.0902\n",
      "Epoch 227/250\n",
      "529/529 [==============================] - 0s 582us/step - loss: 0.1232 - val_loss: 0.0894\n",
      "Epoch 228/250\n",
      "529/529 [==============================] - 0s 573us/step - loss: 0.1232 - val_loss: 0.0876\n",
      "Epoch 229/250\n",
      "529/529 [==============================] - 0s 581us/step - loss: 0.1229 - val_loss: 0.0865\n",
      "Epoch 230/250\n",
      "529/529 [==============================] - 0s 621us/step - loss: 0.1217 - val_loss: 0.0876\n",
      "Epoch 231/250\n",
      "529/529 [==============================] - 0s 633us/step - loss: 0.1219 - val_loss: 0.0878\n",
      "Epoch 232/250\n",
      "529/529 [==============================] - 0s 578us/step - loss: 0.1221 - val_loss: 0.0877\n",
      "Epoch 233/250\n",
      "529/529 [==============================] - 0s 632us/step - loss: 0.1186 - val_loss: 0.0885\n",
      "Epoch 234/250\n",
      "529/529 [==============================] - 0s 569us/step - loss: 0.1209 - val_loss: 0.0886\n",
      "Epoch 235/250\n",
      "529/529 [==============================] - 0s 586us/step - loss: 0.1245 - val_loss: 0.0880\n",
      "Epoch 236/250\n",
      "529/529 [==============================] - 0s 565us/step - loss: 0.1198 - val_loss: 0.0878\n",
      "Epoch 237/250\n",
      "529/529 [==============================] - 0s 579us/step - loss: 0.1196 - val_loss: 0.0884\n",
      "Epoch 238/250\n",
      "529/529 [==============================] - 0s 597us/step - loss: 0.1220 - val_loss: 0.0879\n",
      "Epoch 239/250\n",
      "529/529 [==============================] - 0s 586us/step - loss: 0.1176 - val_loss: 0.0873\n",
      "Epoch 240/250\n",
      "529/529 [==============================] - 0s 587us/step - loss: 0.1193 - val_loss: 0.0876\n",
      "Epoch 241/250\n",
      "529/529 [==============================] - 0s 586us/step - loss: 0.1210 - val_loss: 0.0879\n",
      "Epoch 242/250\n",
      "529/529 [==============================] - 0s 578us/step - loss: 0.1202 - val_loss: 0.0882\n",
      "Epoch 243/250\n",
      "529/529 [==============================] - 0s 550us/step - loss: 0.1186 - val_loss: 0.0885\n",
      "Epoch 244/250\n",
      "529/529 [==============================] - 0s 571us/step - loss: 0.1171 - val_loss: 0.0878\n",
      "Epoch 245/250\n",
      "529/529 [==============================] - 0s 651us/step - loss: 0.1207 - val_loss: 0.0865\n",
      "Epoch 246/250\n",
      "529/529 [==============================] - 0s 595us/step - loss: 0.1196 - val_loss: 0.0877\n",
      "Epoch 247/250\n",
      "529/529 [==============================] - 0s 613us/step - loss: 0.1192 - val_loss: 0.0870\n",
      "Epoch 248/250\n",
      "529/529 [==============================] - 0s 584us/step - loss: 0.1190 - val_loss: 0.0869\n",
      "Epoch 249/250\n",
      "529/529 [==============================] - 0s 600us/step - loss: 0.1195 - val_loss: 0.0876\n",
      "Epoch 250/250\n",
      "529/529 [==============================] - 0s 588us/step - loss: 0.1156 - val_loss: 0.0876\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "0.08756073564291\n"
     ]
    }
   ],
   "source": [
    "data_copy = data.copy()\n",
    "x, y = build_train(data_copy, n_in, n_out)\n",
    "x_train, y_train, x_val, y_val = split_data(x, y, n_val)\n",
    "model = build_lstm(n_in, 1)\n",
    "model = model_fit(x_train, y_train, x_val, y_val, 1)\n",
    "predict = model.predict(x_val)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([629652.06, 601111.1 , 580061.75, 553453.94, 522222.25, 496213.34,\n",
       "       487906.25, 484180.66, 486937.56, 498088.5 , 512388.75, 527296.56,\n",
       "       550159.8 , 580237.  , 612543.5 , 646128.7 , 680854.56, 705669.2 ,\n",
       "       716264.9 , 732494.75, 725075.06, 710319.5 , 689452.56, 662935.2 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# predict = model.predict(x_val)  \n",
    "validation = scaler.inverse_transform(predict)[0]\n",
    "validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([639457., 620449., 611584., 493739., 323143., 329209., 350783.,\n",
       "       403793., 449446., 489899., 528206., 563321., 594820., 608519.,\n",
       "       616327., 626740., 640685., 673864., 739997., 789165., 818590.,\n",
       "       804708., 748938., 694595.])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual = scaler.inverse_transform(y_val)[0]\n",
    "actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4500x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict = validation\n",
    "actual = actual\n",
    "x = [x for x in range(24)]\n",
    "fig, ax = plt.subplots(figsize=(15,5),dpi = 300)\n",
    "ax.plot(x, predict, linewidth=2.0,label = \"predict\")\n",
    "ax.plot(x, actual, linewidth=2.0,label = \"actual\")\n",
    "ax.legend(loc=2);\n",
    "# ax.set_title(bf_name)\n",
    "plt.ylim((0, 900000))\n",
    "plt.grid(linestyle='-.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8520669472132617"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ACC\n",
    "error = 0\n",
    "summery = 0\n",
    "for i in range(24):\n",
    "    error += abs(predict[i] - actual[i])\n",
    "    summery += actual[i]\n",
    "acc = 1 - error/summery\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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